In conventional television an image is scanned by a television camera and transmitted, in serial, pixel by pixel. The transmission channel bandwidth of such transmission can be computed approximately as the product of the screen rows, screen columns, scan frequency, colors and brightness resolution. Any improvement in screen size, image resolution or colors, as desired for High Resolution Television HDTV, will quickly crowd the precious channels in the broadcast spectrum reserved for television. HDTV with larger screens and better image resolution using present TV channels is not possible according to basic (Shannon) information theory.
In True Information Television TITV the image data from the television camera is compressed in two ways before transmission. First, only the moving or changing portion of the image is encoded for transmission. Portions of the image which do not change from scan to scan can be stored or remembered by the receiver and need not be re-transmitted at every scan. Second, the image is compared with previously learned image patterns to generate large partial images, called superpixel, which represent large portions of the screen image. A patch of blue sky in the image, for example, does contain very little true information and can be combined into a large superpixel which may represent a very large image portion on the screen. The input image from the camera is compared with previously learned data in a pyramid type progression leading to larger and larger superpixel. Whatever images were previously learned will be converted into high level superpixel representing successively larger portion on the screen. Before any transmission the system must be shown common images via the television camera to learn or absorb the pattern in a process described as the assembly of a parallel infinite dimensional network. Infinite dimensional networks are part of a mathematical theory of learning which were previously published by the inventor. Every new learning process will produce a very unique infinite dimensional network which affects the superpixel code. This may be exploited for encrypted transmissions in a virtually unbreakable code.
True information is believed by the inventor to be equivalent to information perceived by our own brain and eyes. An enlargement of a photograph image, for example, does not provide more information to our own mind. What provides true information is the novelty of the objects in the image because known or familiar objects no matter how large or complex provide only little information. True information is also provided by movement within the image and a stationary image will quickly loose its novelty. The inventor also believes that our eyes and brain have a limited true information bandwidth equivalent to true information television transmission. Any true information transmission in excess of the true information bandwidth of our own eyes and brain would be wasted. A complex pattern on the screen, for example, which moves too fast need not be faithfully reproduced because our own eyes and mind would only perceive it as a blur.
In addition to providing a new kind of digital television, the system can also be used for electronic roboter vision if combined with other self-learning infinite dimensional networks. An object or shape shown to the television camera will produce a code which uniquely identifies that shape or object. The code can later be used to retrieve the shape or object on a television monitor.
A quantum of true information, as expressed in a superpixel, must have two types of information which are: the Pattern address and the Network address. The Pattern address will provide information to answer the question "What is it ? " while the Network address will provide information to answer the question of "Where is it ? " on the screen.
True Information Television TITV may greatly advance the search for a High Definition Television HDTV for the next generation television. It may also provide a way toward roboter Vision in Artificial Intelligence AI.